View Chapter:
Downloads:
Chapter 4
Classic features of microcredit
In this section, we discuss recent literature that seeks to unpack some of the most important and distinct features of the classic microcredit model.
Group lending
Group lending is one of the most distinctive features of the classic microcredit model. In such arrangements, loans are typically made to individuals, but there is joint liability within a small group. Early theoretical work demonstrated the benefits of joint-liability group lending in mitigating the problems of adverse selection and moral hazard for MFIs, by providing peer screening, monitoring, and an enforcement mechanism that exploits local information (Stiglitz 1990, Varian 1990, Besley and Coate 1995, Ghatak and Guinnance 1999, Ghatak 2000). Initial empirical studies provided suggestive correlations that were consistent with this; for example, Cull et al. (2007) use data from 124 lenders collected by the Microfinance Information Exchange (MIX) and find that lenders using group lending methods faced lower levels of default. Giné et al. (2010) took the empirical analysis a step further, by implementing ‘microfinance games’: framed field experiments with microenterprise owners in Peru. They find that, consistent with the theory, joint liability did increase loan repayment rates. Related lab evidence was provided by Fischer (2013), who found that some individuals ‘free ride’ on their partners’ insurance when part of a joint liability group, with the problem particularly severe in environments where there was imperfect information (where fellow group members were only aware of the final outcome of the investment). Further lab evidence on potential negative consequences of joint liability (in terms of excessive peer punishment) is provided by Czura (2015) and Czura et al. (2020).
Giné and Karlan (2014) provide one of the first major pieces of evidence from a large field experiment. They conducted two RCTs with a large bank in the Philippines, with the aim of evaluating the efficacy of group liability microcredit (relative to individual liability) on the monitoring and enforcement of loans. In the first experiment, half of the bank’s existing group-lending centres were randomly converted to individual liability (while maintaining all other logistical features of group lending, such as sharing of a common meeting location and payment methods). The design also allowed them to separate selection from moral hazard, since clients had already been screened for group loans, and what was being tested was whether – after peer screening – group liability had any additional effect on the mitigation of moral hazard through improved monitoring or enforcement. Note that this also limits policy interpretation, since individuals selected under group liability may be different (for example, more likely to repay) to those that would have hypothetically been selected under an individual liability product. For this reason, the authors also conduct a second RCT with members who joined the programme after the bank’s removal of the joint liability clause, as the bank expanded into new areas. In the second trial, villages were randomly offered group liability, individual liability, or phased-in individual liability (which started with joint liability and then converted to individual liability after successful completion of one loan cycle). The second experiment therefore combines selection, monitoring, and enforcement, and is less precise in testing mechanisms but more policy relevant.
From the first experiment, using data collected over three years, the authors find no change in repayment rates for borrowers who had their loans converted to individual liability, and find that it did not administratively cost more for the bank to implement individual liability. From the second experiment, the authors also do not find any differences in repayment rates, but do find that credit officers are less likely to create groups under individual liability, and qualitative evidence suggests that this was driven by an unwillingness to extend credit without guarantors. The authors discuss whether their finding of no deterioration in default rates under individual liability contradicts the theoretical predictions of adverse selection from earlier models. They argue that – even without joint liability – groups nonetheless leveraged sufficient social capital to ensure good repayment. This “peer pressure without legal pressure” can come about from a range of other features, such as public repayments, increasing loan sizes, and frequent instalments (discussed below).
Further empirical evidence comes from the previously mentioned field experiment of Attanasio et al. (2015), where villages in Mongolia were randomly assigned to group loans, individual loans, or no loans. Importantly, neither the group nor the individual lending programmes included mandatory public repayment meetings (as opposed to the aforementioned experiment by Giné and Karlan, where individual liability lending still contained ‘group lending features’). The authors also find no evidence of any difference in default rate.[1]
While many microfinance institutions have moved towards individual-liability loans, others continue to use explicit joint liability or other aspects of the group lending model that leverage peer pressure. Further, there continues to be interesting work on the impacts of this most quintessential feature of microcredit. For example, Attanasio et al. (2019) show (theoretically and empirically) that in general when investment risk is higher – as measured by a high average variance of subjective risk perceptions – the probability of taking up a loan is significantly lower. The authors provide evidence that joint-liability loans (compared to individual-liability loans) can reduce this discouraging effect of project risk on loan take up, and that risk-averse borrowers may value the insurance aspect of joint-liability microcredit contracts.[2] The authors conclude with a note of caution that a continuation of the trend towards liability individualisation may be beneficial to less risk-averse borrowers but may exclude some more risk-averse borrowers from the market for formal financial services, and preclude the financing of productive activities.
Dynamic incentives
In the previous sub-section, we noted the surprising empirical result from the experimental literature: moving to individual-liability lending does not appear to have a significant effect on the default rates of clients. This comes in spite of a number of influential theoretical papers that have demonstrated the benefits of group lending in terms of mitigating adverse selection and moral hazard. One way of reconciling these results is in the fact that while many MFIs have moved away from explicit joint liability, many retained the group lending features that leveraged social capital and other incentives to maintain high repayments (de Quidt et al. 2016).[3] One of the most commonly cited features that was retained was ‘dynamic incentives’, which we discuss in this section.
Dynamic incentives refer to the process of ‘incremental lending’: providing initial small loans, with access to larger loans (and potentially better loan terms, in some settings) conditional on good repayment behaviour, with exclusion from future loans otherwise. The theoretical literature has long argued about the benefits of dynamic incentives for maintaining high repayment rates (Besley 1995).[4] However, with the proliferation of MFIs and increased competition, the power of dynamic incentives is called into question, particularly in urban areas with high mobility of populations (Morduch 1999).[5]
Giné et al. (2010) explicitly tested the impact of dynamic incentives in their framed field experiment with Peruvian microentrepreneurs. In it, they find that adding dynamic incentives to any loan contract does decrease the rate of default. Moving from the lab to the field, Giné et al. (2012) use an experiment to explore an intervention (fingerprint identification) that improved the lending bank’s ability to implement dynamic repayment incentives, allowing it to withhold future loans from past defaulters while rewarding good borrowers with better loan terms. This paper was written around the time of the microfinance crisis in Andhra Pradesh, India, in 2011, after which client protection was high on the policy agenda with increasing calls for MFIs to participate in credit bureaus. For credit bureaus to function effectively, one needs to be able to identify individuals with reasonable certainty. In their experiment, Giné et al. (2012) randomise fingerprinting of loan applications in Malawi to test the impact of improved personal identification. The authors also develop a theoretical model that demonstrates how dynamic incentives (specifically, the ability to deny credit in later periods based on prior repayment performance) can reduce both adverse selection and moral hazard. Data from their field experiment demonstrated that fingerprinting led to substantially higher repayment rates for the sub-group of borrowers with the highest ex-ante default risk (based on a credit score prior to the experiment). The authors suggest that fingerprinting, by improving personal identification, enhanced the credibility of the lender’s dynamic incentive. They also find that fingerprinting led farmers to choose smaller loan sizes (i.e. a reduction in adverse selection), and that high-default-risk farmers who were fingerprinted also diverted fewer inputs away from the crop that they were supposed to be farming (i.e. a reduction in moral hazard).
A related paper is the consumer credit experiment of Karlan and Zinman (2009) in South Africa. While Giné et al. (2012) had manipulated the credibility of dynamic incentives, Karlan and Zinman (2009) informed borrowers of the existence of dynamic incentives using a field experiment with a major South African lender. The authors randomised 58,000 direct mail offers to former clients while varying interest rates and dynamic incentives. Their particular dynamic incentive involved making the interest rate on future loans conditional on the repayment behaviour on the current loan. They find that clients offered dynamic incentives defaulted an estimated 13 to 21% less than those who were not.
Targeting female borrowers
As discussed earlier in this review, informal pressure has been an important aspect of any discussion of microcredit; indeed, the traditional group-lending model is one that relies heavily on informal community pressure as a way of ensuring loan repayment. In recent years, however, the empirical literature has gone further: testing how sharing norms may limit the efficacy of credit for borrowers by acting as an informal tax on the capital that loan products provide. Specifically, several recent papers emphasise the importance of intra-household sharing norms and, in particular, show novel ways in which financial products can help women to protect their personal wealth.
The recent work of Bernhardt et al. (2019) is a key contribution for thinking about returns to capital and sharing norms. Bernhardt et al. re-analyse data from three earlier experiments: the RCT of Field et al. (2013) (which we summarise below), as well as the ‘capital drop’ experiments of de Mel et al. (2008) and Fafchamps et al. (2014). Each of the three papers found stronger average effects for male-owned enterprises than for female-owned. In each case, Bernhardt et al. show important effect heterogeneity among the group of female respondents, comparing those in single-enterprise households with those in multiple-enterprise households. As the authors explain, “when both male and female entrepreneurs are present, households direct more capital toward male relative to female-owned investment opportunities”. Similarly, Fiala (2018) re-analyses results from a two-year follow-up survey on a field experiment that provided loans, grants, and training to microenterprise owners in Uganda. Fiala shows important effect heterogeneity by behaviour in a ‘hiding game’ played with respondents, arguing that this is “consistent with women having little control over resources, and so hiding money is the only way to retain control”.[6]
This key insight – that microcredit might have important impact heterogeneity based on intra-household norms – resonates with two recent experiments on the design of financial products. Field et al. (2021) randomly varied whether wages paid to women in rural India from the National Rural Employment Guarantee Scheme were deposited into a woman’s personal bank account or into the account of the male household head. The authors find that women who received pay into their personal account increased their labour supply – both in public and private sectors – and that their husbands reported fewer social costs to having a wife who works. While not a microcredit experiment as such, this paper is nonetheless highly relevant to the design of financial products, showing that innovation in financial design can help to empower women in low-income contexts.
Riley (2024) takes these ideas to the domain of microcredit. Riley reports results from an experiment involving 3,000 female clients of BRAC in Uganda, in which some respondents were randomly assigned to receive their microcredit lump sum in a mobile account, whereas others received the lump sum in cash.[7] Eight months after dispersal, Riley finds that women who received the lump sum in a mobile account had, on average, 15% higher profits and 11% more business capital. Further, she shows significant heterogeneity by an index of baseline sharing pressure, such that those who were subject at baseline to greater family sharing pressures enjoyed significantly larger benefits from the disbursal into a mobile account.[8] Further evidence is provided by Heath and Riley (2024), who conduct an experiment in Tanzania that switched women’s microfinance groups from cash to mobile money for loan repayments. This shift not only increased women’s control over finances but also enhanced their household decision-making power and led to a reallocation of expenditures towards items more aligned with women’s preferences. Their findings underscore the empowerment potential of integrating digital financial tools into microcredit programmes, offering women greater autonomy and financial control within the household.[9]
Recent evidence from India suggests that microcredit groups can empower women politically by expanding their networks outside the household. Prillaman (2023) finds that women participating in Self-Help Groups (SHGs) are more likely to engage in non-electoral forms of political participation, such as attending village meetings and making claims on local government. This political empowerment appears to result from SHGs’ role in building social networks and collective action skills, rather than economic empowerment alone, highlighting a pathway through which microcredit groups can contribute to women’s autonomy and civic engagement.
Timing of loan and repayment
Another prominent feature of the classic microcredit model is the required frequency of repayments. Compared to most loan products around the world, microcredit loans are characterised by very high frequency repayment requirements – with repayment biweekly or even weekly quite common. Despite their higher transaction costs, many MFIs have traditionally made strong claims about the benefits of regular repayment schedules, often framed in terms of inculcating ‘fiscal discipline’ for borrowers and maintaining high repayment rates. For example, regular repayments are hypothesised to benefit the ‘screening out’ of undisciplined borrowers because loan officers and peer groups get an early warning from those borrowers about potential future problems. Frequent repayment of small sums can also help borrowers make their payments without needing to accumulate large sums of cash at home, which can be difficult due to sharing pressure (discussed above) or self-control issues (discussed below).
Fischer and Ghatak (2016) take a closer look at the theoretical underpinnings of high-frequency repayment. Their starting point is that “the pervasive belief among practitioners that frequent repayment is critical in achieving high repayment rates is puzzling. Classically rational individuals should benefit from more flexible repayment schedules, and less frequent repayment should increase neither default not delinquency”. The authors propose a theoretical explanation for the purported benefit of fiscal discipline using the concept of ‘present bias preferences’. Intuitively, when borrowers are present biased, the immediate gain to defaulting on any large repayment is subject to significant temptation. When these payments are spread out, the instantaneous repayment burden at any time is smaller and less subject to temptation. But the authors highlight a trade-off: frequent repayment means that at the time of the first payment, the rewards (access to future credit) are further away from the repayment decision, and thus more heavily discounted.[10]
The theoretical literature highlights the ambiguous effect of repayment frequency. Field and Pande (2008) set out with the empirical question: do lower-frequency payments affect the probability of loan default? The authors implement a field experiment in urban India with one hundred groups, each consisting of ten first-time borrowers. Each group was randomly assigned to either a weekly or monthly repayment schedule (after group formation had been completed and clients approved for the loan). The headline result is that switching from weekly to monthly instalments did not affect client repayment capacity, with delinquency rates low and not significantly different across clients on weekly and monthly repayment schedules. The authors argue that switching to lower frequency repayment schedules could allow MFIs to save dramatically on the transaction costs of instalment collection while facing no additional default risk. (In a later study, Field, Pande, and co-authors also make an important contribution in exploring a related but distinct form of flexibility (‘repayment grace periods’); we discuss this in the next subsection.)
Another important aspect of ‘timing’ is the timing of disbursal of the loan principal. As highlighted by Morduch (1999), one implication of the classic model is that, since repayment begins before any feasible investment of the funds bears fruit, the classic microcredit contract appears to be inappropriate for households without a diversified and steady income stream (against which MFIs are implicitly lending), particularly for households exposed to highly seasonable occupations such as agriculture. A recent field experiment by Burke et al. (2019) sheds light on this. The authors demonstrate that lack of access to credit for farmers limits their ability to deal with large and regular fluctuations in local grain prices over time, which often forces farmers to “sell low and buy high”. The authors document that grain prices regularly rise by 25 to 40% between the harvest and lean seasons, and often by more than 50% in isolated markets.[11] Their starting point is the seemingly puzzling behaviour of many farmers, who – despite having access to relatively cheap storage facilities – tend to sell their crops immediately after harvest (when prices are low) and then, several months later during the lean season, return to the market as customers once prices have risen. The authors posit that financial market imperfections contribute to the apparent inability to exploit this arbitrage opportunity, and having to sell grain at low post-harvest prices in order to meet urgent cash needs (e.g. to pay school fees), then buying it back a few months later at higher prices to meet consumption needs. In essence, households use the grain market as a high-interest lender of last resort. The authors work with a local agricultural NGO and randomly offer some smallholder maize farmers a loan at harvest. They found that farmers offered the harvest-time loan sold significantly less maize in the period immediately following harvest, which led to a large increase in revenues for households. Other evidence on the interaction between liquidity constraints, seasonality, and credit timing is provided by Shonchoy (2014), Casaburi and Willis (2018), Fink et al. (2020) and Beaman et al. (2023).
Repayment flexibility and inflexibility
Standard microcredit contracts typically require regular repayments at a fixed frequency – usually beginning shortly after the initial disbursement of the lump sum. As we noted in the previous subsection, it is not difficult to see why such contracts might be attractive to lenders: by requiring their clients to commit to a regular repayment schedule, lenders may feel more confident that they are attracting responsible borrowers and encouraging those borrowers to be more faithful as clients. However, it is not at all clear that such contractual inflexibility is helpful for borrowers; indeed, it may be that contracts can help borrowers by incorporating some option for repayment flexibility.
The seminal paper on this issue is the work of Field et al. (2013). Field et al. worked with a sample of 845 women in low-income urban neighbourhoods in Kolkata who received microloans ranging in size from about US$ 90 to 225 to be repaid fortnightly over a total of 44 weeks. The authors randomised these clients into two groups: a control group, who were obliged to start their repayments two weeks after disbursement, and a treatment group, who were obliged to start repaying only after a two-month ‘grace period’. This grace period generates substantial and sustained benefits for clients. In the short run, microenterprise investment among the treatment group was about 6% higher than in the control group. In a long-run follow-up conducted nearly three years after disbursement, treated clients enjoyed significantly higher business profits (a 41% increase in weekly profits) and greater monthly household income (an increase of about 20%). Field et al. interpret their results as showing that the grace period allowed clients to accumulate a larger initial lump sum, and therefore facilitated investment in higher-return lumpy assets. The authors find important heterogeneity in their estimated profit impacts: effects are larger for the most risk-averse clients and for those with fewer means of dealing with short-term liquidity needs (proxied here by respondents having a chronically sick member of their household). It is worth noting, however, that the authors also find higher default rates among the treated group: specifically, about 9% of clients in the treatment group had failed to repay 24 weeks after the loan was due, compared to only 2% in the control group. In follow-up work, Agte et al. (2024) provide evidence of positive long-term impacts on households’ human capital investments, demonstrating that initial business growth among treated households led to significant educational gains for their children over an 11-year period, with effects varying based on parental literacy levels.
Battaglia et al. (2024) present an innovative variation on the idea of a grace period: they allow the grace period to be taken at a time of the client’s choosing. This allows the authors to distinguish between the two main advantages that repayment flexibility might confer, namely: (i) flexibility can help to ease credit constraints (by allowing clients additional time to repay, and thus providing an opportunity to accumulate a larger lump sum – as in the work of Field et al. (2013)), and/or (ii) flexibility can offer implicit insurance (by allowing clients to defer repayment if facing an adverse shock). To distinguish these mechanisms, Battaglia et al. conduct a field experiment among clients of BRAC in Bangladesh. Clients in the control group received a loan to be repaid over a 12-month cycle, with monthly instalments of equal size. Clients in the treatment group were provided with two vouchers to be used at times of the clients’ choosing. Each voucher, when used, would allow a client to defer one month’s repayment (thus extending the total loan cycle). As the authors explain, this provides clients with a direct choice between accumulating a larger lump sum (specifically, by using the two vouchers in the first two months), and enjoying implicit insurance (by holding the vouchers, to be used if negative shocks were to strike).
Battaglia et al. find strong evidence for the second of these motives, as well as substantial overall benefits from the vouchers as a consequence. Specifically, voucher usage is indeed dispersed over the loan cycle: only about 2% of clients use the vouchers in months one and two, and 40% of clients do not use any voucher at all, despite having taken up the flexible contract. On average, treated clients have business assets worth 51% more than those of the control group. As the authors explain, those treated clients “generate 87% more revenues, have 25% larger profits, and experience 80% higher sales volatility”. Impacts are concentrated among poorer borrowers on traditional (uncollateralised) contracts, which have an average loan size of about US$ 275. Unlike Field et al. – and consistent with the implicit insurance provided by the flexible vouchers – Battaglia et al. find that their treated clients have a lower probability of default.
In related work, Barboni and Agarwal (2023) report results from a field experiment in Uttar Pradesh, India, conducted with a group of (mostly male) borrowers who had just applied for their first individual loan (having previously participated in joint-liability products). Clients in the control group were offered a 24-month fixed-repayment loan for about US$ 500, with an interest rate of 24%. In the treatment group, clients were allowed to choose between that standard contract and a contract with additional flexibility. Specifically, the flexible contract was offered at an interest rate of 26% and included an option to exercise a ‘repayment holiday’ of three consecutive months.[12] Upon taking the repayment holiday, the monthly repayment obligations would be re-calculated to ensure that the contract’s end date was not affected. As the authors explain, their flexible contract “can be thought of as a ‘line of credit’ available to borrowers”.[13] Barboni and Agarwal have three key results. First, they find no difference in the probability of late repayment, but a large and significant increase (from 30% to about 40%) in the probability of having repaid the loan early. Second, the authors find an improvement in several measures of business performance, including in sales and profits. Finally, the authors are able to describe carefully the characteristics that predict opting into the more flexible contract: groups that are more likely to opt for flexibility are “time consistent borrowers, those who have an appetite for risk, those who report being more worried about future expenses, but also those who report giving financial advice to others”.
The previous two papers found that offering flexible-repayment loans to existing borrowers led to improved business performance and loan repayment. In a recent paper, Brune et al. (2022) provide a different perspective, by exploring the established microlender practice of offering rigid contracts to first-time borrowers. They partnered with a Colombian lender that offered first-time borrowers a flexible loan that permitted delaying up to three monthly repayments. A group of prospective borrowers was offered and disbursed the flexible credit, another was offered the standard credit but then surprised with the flexible credit at disbursement, and one last group of borrowers was offered and disbursed the standard rigid credit. This design allowed the authors to test both for selection effects as well as contract effects on choices and outcomes after borrowing (in a similar spirit to the aforementioned work of Karlan and Zinman 2009). The paper finds no differences in the take-up rates, characteristics, or outcomes of the group that was offered and disbursed flexible credit compared to the group that was offered the standard contract but then had flexible credit disbursed. The authors argue that the lack of selection effects suggests only a small share of profitable entrepreneurs would reject the standard contract but accept the flexible contract. In addition, they find that flexibility increases default among first-time borrowers, which offers a cautionary tale about providing flexibility to inexperienced borrowers. The evidence thus aligns with the established microlender practice of offering rigid contracts to first-time borrowers. Interestingly, the authors report that the lender later introduced a modified version of the flexible loan for non-study clients, but that only credit officers (and not clients) decided when to use a pass (and clients were not made aware of this feature ahead of time). They report that pass use thus became merely a tool for credit officers to adjust default and pursue enforcement and refinancing when needed.
McIntosh et al. (2023) explore the impact of structured commitment contracts in the context of pawnshop lending in Mexico. Specifically, McIntosh et al. run an RCT with three arms: (i) a control group with flexible terms, (ii) a ‘choice’ group with the option to opt into structured repayments, and (iii) a ‘forced’ group, required to make three monthly payments. The authors find that borrowers in the forced group saw a 22% reduction in financial costs, a 15% increase in asset recovery, and a 19% rise in repeat borrowing. This suggests that structured repayment schedules can enhance borrower outcomes—even for those who would not have voluntarily chosen them. This insight challenges the assumption that voluntary choice always maximises welfare, and supports what McIntosh et al. term “private paternalism,” where rigid contracts may better serve some borrowers’ long-term interests. The authors note that their findings contrast with some of the microfinance studies we discussed above, which did not suggest similar benefits from rigid payments. The authors attribute this difference to the fact that microfinance contracts are already highly structured, and to the fact that borrowers in those settings had already self-selected into structured lending (unlike the high-default setting that they study). Their work provides a novel insight into both take-up and the efficacy of voluntary commitment mechanisms.
Continuing on the theme of ex-post flexibility for defaulting clients, Fiorin et al. (2022) explore how borrowers respond to a debt moratorium. Debt moratoria allow borrowers to postpone loan payments and are frequently used to soften the impact of economic crises, even in contexts where forbearance is not mandated. However, one concern is that moratoria might give rise to moral hazard, by changing borrower beliefs about credit enforcement and the likelihood of future relief. The authors partner with a large consumer lender in India to issue randomised debt forbearance offers to a nationwide sample of borrowers. In the experiment, borrowers receive identical forbearance offers that are presented either as an act of generosity by the lender or as the result of government regulation. They find that delinquent borrowers who are offered a debt moratorium by their lender are 4 percentage points less likely to default on their loan, while forbearance has no effect on repayment if it is granted by the regulator. Borrowers receiving forbearance offers from their lender are also more likely to do future business with the lender: in a follow-up experiment they find that demand for a non-credit product offered by the lender is 10 percentage points higher among customers who were offered repayment flexibility by the lender than among customers who received a moratorium offer presented as an initiative of the regulator. Overall, their results suggest that, rather than generating moral hazard, repayment flexibility through debt forbearance can improve loan repayment and strengthen banking relationships.
We have seen evidence that payment flexibility can be beneficial in some circumstances, such as for graduated borrowers who may value contractual innovations that allow them to better match their loan repayments to their underlying income flows and liquidity requirements.[14] On the other hand, as discussed in Brune et al. (2022), a rigid repayment structure may help other borrowers in fostering discipline in the face of temptations or procrastination. There is evidence that such fiscal discipline can be important for some clients due to its implicit commitment value: regular repayments of small sums can help such clients in the accumulation of a valuable lump sum (see, for example, Rutherford 2000, Collins et al. 2009, Morduch 2010, Bauer et al. 2012, and Afzal et al. 2018). The trade-off between these objectives – and the question of which innovations to offer, when, and to whom – remains an important open area of research in contract design.[15]
References
Afzal, U, G D’Adda, M Fafchamps, S Quinn and F Said (2018), “Two Sides of the Same Rupee? Comparing Demand for Microcredit and Microsaving in a Framed Field Experiment in Rural Pakistan”, The Economic Journal 128(614): 2161–2190.
Afzal, U, G D’Adda, M Fafchamps, S Quinn and F Said (2024), “Demand for Commitment in Credit and Saving Contracts: A Field Experiment”, The Economic Journal 134(664): 3063–3095.
Ahlin, C, and G Debrah (2022), “Group lending with covariate risk.” Journal of Development Economics 157: 102855.
Aker, J C, R Boumnijel, A McClelland and N Tierney (2016), “Payment Mechanisms and Antipoverty Programs: Evidence from a Mobile Money Cash Transfer Experiment in Niger”, Economic Development and Cultural Change 65(1): 1-37.
Aragón, F M, A Karaivanov and K Krishnaswamy (2020), “Credit lines in microcredit: Short-term evidence from a randomized controlled trial in India,” Journal of Development Economics 146(102497).
Attanasio, O, B Augsburg, R De Haas, E Fitzsimons and H Harmgart (2015), “The Impacts of Microfinance: Evidence from Joint-Liability Lending in Mongolia”, American Economic Journal: Applied Economics 7(1): 90–122.
Attanasio, O, B Augsburg and R De Haas (2019), “Microcredit Contracts, Risk Diversification and Loan Take-up”, Journal of the European Economic Association 17(6): 1797–1842.
Baland, J M, C Guirkinger and C Mali (2011), “Pretending to Be Poor: Borrowing to Escape Forced Solidarity in Cameroon,” Economic Development and Cultural Change 60(1): 1-16.
Barboni, G and P Agarwal (2023), “How Do Flexible Microfinance Contracts Improve Repayment Rates and Business Outcomes? Experimental Evidence from India”, Available at SSRN 4358795.
Battaglia, M, S Gulesci and A Madestam (2024), “Repayment Flexibility and Risk Taking: Experimental Evidence from Credit Contracts”, Review of Economic Studies 91(5): 2635–2675.
Bauer, M, J Chytilová and J Morduch (2012), “Behavioral Foundations of Microcredit: Experimental and Survey Evidence from Rural India”, American Economic Review 102(2): 1118–39.
Beaman L, D Karlan, B Thuysbaert and C Udry (2023), “Selection into Credit Markets: Evidence from Agriculture in Mali”, Econometrica 91(5): 1595–1627.
Bernhardt, A, E Field, R Pande and N Rigol (2019), “Household Matters: Revisiting the Returns to Capital among Female Microentrepreneurs”, American Economic Review: Insights 1(2): 141–160.
Besley, T (1995), “Savings, credit and insurance”, in J Behrman and T N Srinivasan (eds), Handbook of Development Economics Vol. 3A, Elsevier, pp. 2123–2207.
Besley, T and S Coate (1995), “Group Lending, Repayment Incentives and Social Collateral”, Journal of Development Economics 46(1): 1–18.
Brune, L, X Giné, and D Karlan (2022), “Give Me a Pass: Flexible Credit for Entrepreneurs in Colombia”, National Bureau of Economic Research Working Paper No. 30634.
Bulow, J and K Rogoff (1989), “Sovereign Debt: Is to Forgive to Forget?”, American Economic Review 79(1): 43-50.
Burchardi, K B, S Gulesci, B Lerva, and M Sulaiman (2019), “Moral hazard: Experimental evidence from tenancy contracts.” The Quarterly Journal of Economics 134(1): 281– 347.
Burke, M, L F Bergquist and E Miguel (2019), “Sell Low and Buy High: Arbitrage and Local Price Effects in Kenyan Markets”, The Quarterly Journal of Economics 134(2): 785–842.
Breza, E, M Kanz, and L F Klapper (2024), "Learning to Navigate a New Financial Technology." Journal of Finance, forthcoming.
Cardell, L and H Michelson (2022), "Price risk and small farmer maize storage in Sub-Saharan Africa: New insights into a long-standing puzzle", American Journal of Agricultural Economics.
Casaburi, L and J Willis (2018), “Time versus State in Insurance: Experimental Evidence from Contract Farming in Kenya”, American Economic Review 108(12): 3778–3813.
Collins, D, J Morduch, S Rutherford and O Ruthven (2009), Portfolios of the Poor: How the World’s Poor Live on $2 a Day, Princeton; Oxford: Princeton University Press.
Cordaro, F, M Fafchamps, C Mayer, M Meki, S Quinn and K Roll (2022), “Microequity and mutuality: Experimental evidence on credit with performance-contingent repayment” NBER Working Paper No. w30411.
Cull, R, A Demirguç‐Kunt and J Morduch (2007), “Financial performance and Outreach: A Global Analysis of Leading Microbanks”, The Economic Journal 117(517): F107–F133.
Czura, K (2015), “Pay, peek, punish? Repayment, information acquisition and punishment in a microcredit lab-in-the-field experiment”, Journal of Development Economics 117: 119–133.
Czura, K, A John and L Spantig (2020), “Flexible Microcredit: Effects on Loan Repayment and Social Pressure”, CESifo Working Paper No. 8322.
De Mel, S, D McKenzie and C Woodruff (2008), “Returns to Capital in Microenterprises: Evidence from a Field Experiment”, The Quarterly Journal of Economics 123(4): 1329–1372.
De Mel, S, D McKenzie and C Woodruff (2019), “Micro-equity for microenterprises.” CEPR Discussion Paper No. DP13698.
De Quidt, J, T Fetzer and M Ghatak (2016), “Group lending without joint liability”, Journal of Development Economics 121: 217–236.
De Quidt, J, T Fetzer and M Ghatak (2018a), “Commercialization and the decline of joint liability microcredit”, Journal of Development Economics 134: 209-225.
De Quidt, J, T Fetzer and M Ghatak (2018b), “Market Structure and Borrower Welfare in Microfinance”, The Economic Journal 128(610): 1019-1046.
Fafchamps, M, D McKenzie, S Quinn and C Woodruff (2014), “Microenterprise growth and the flypaper effect: Evidence from a randomized experiment in Ghana”, Journal of Development Economics 106(C): 211–226.
Field, E and R Pande (2008), “Repayment Frequency and Default in Microfinance: Evidence from India”, Journal of the European Economic Association 6(2-3): 501–509.
Field, E, R Pande, J Papp and N Rigol (2013), “Does the Classic Microfinance Model Discourage Entrepreneurship among the Poor? Experimental Evidence from India”, American Economic Review 103(6): 2196–2226.
Field, E, R Pande, N Rigol, S Schaner, C Troyer Moore (2021), “On her own account: How strengthening women's financial control impacts labor supply and gender norms.”, American Economic Review 111(7): 2342–75.
Fiala, N (2018), “Returns to microcredit, cash grants and training for male and female microentrepreneurs in Uganda”, World Development 105: 189–200.
Fischer, G (2013), “Contract Structure, Risk-Sharing, and Investment Choice”, Econometrica 81(3): 883–939.
Fischer, G and M Ghatak (2016), “Repayment frequency in lending contracts”, Mimeo.
Fink, G, B K Jack and F Masiye (2020), “Seasonal Liquidity, Rural Labor Markets, and Agricultural Production”, American Economic Review 110(11): 3351–92.
Fiorin, S, J Hall and M Kanz (2022), “How do Borrowers Respond to a Debt Moratorium? Experimental Evidence from Consumer Loans in India”, Working paper.
Friedson-Ridenour, S and R S Pierotti (2019), “Competing Priorities: Women’s Microenterprises and Household Relationships”, World Development 121: 53–62.
Ghatak, M (2000), “Screening by the Company You Keep: Joint Liability Lending and the Peer Selection Effect”, The Economic Journal 110(465): 601–631.
Ghatak, M and T W Guinnane (1999), “The Economics of Lending with Joint Liability: Theory and Practice”, Journal of Development Economics 60(1): 195–228.
Giné, X and D S Karlan (2014), “Group versus individual liability: Short and long term evidence from Philippine microcredit lending groups”, Journal of Development Economics 107: 65–83.
Giné, X, J Goldberg and D Yang (2012), “Credit Market Consequences of Improved Personal Identification: Field Experimental Evidence from Malawi”, American Economic Review 102(6): 2923–54.
Giné, X, P Jakiela, D Karlan and J Morduch (2010), “Microfinance Games”, American Economic Journal: Applied Economics 2(3): 60–95.
Heath, R and E Riley (2024), “Digital Financial Services and Women’s Empowerment: Experimental Evidence from Tanzania”, Working Paper.
Karlan, D and J Zinman (2009), “Observing Unobservables: Identifying Information Asymmetries with a Consumer Credit Field Experiment”, Econometrica 77(6): 1993–2008.
Jain, S and G Mansuri (2003), “A little at a time: the use of regularly scheduled repayments in microfinance programs”, Journal of Development Economics 72(1):253-279.
Jakiela, P and O Ozier (2016), “Does Africa Need a Rotten Kin Theorem? Experimental Evidence from Village Economies”, Review of Economic Studies 83(1): 231-268.
Lane, G (2024), “Adapting to Climate Risk with Guaranteed Credit: Evidence from Bangladesh”, Econometrica 92(2): 355-386.
Mahmud, M (2020), “Repaying Microcredit Loans: A Natural Experiment on Liability Structure”, The Journal of Development Studies 56 (6): 1161-1176.
McIntosh, C, I Meza, J Sadka, E Seira, and F J DiTraglia (2023), “The controlled choice design and private paternalism in pawnshop borrowing”, Working Paper.
Morduch, J (1999), “The Microfinance Promise”, Journal of Economic Literature 37(4): 1569–1614.
Morduch, J (2010), “Borrowing to Save”, Journal of Globalization and Development 1(2): 1–11.
Prillaman, S A (2023), “Strength in numbers: how women's groups close India's political gender gap”, American Journal of Political Science 67(2): 390–410.
Riley, E (2024), “Resisting Social Pressure in the Household Using Mobile Money: Experimental Evidence on Microenterprise Investment in Uganda.”, American Economic Review 114(5): 1415–47.
Rutherford, S (2000), “Raising the curtain on the ‘microfinancial services era’”, Small Enterprise Development 11(1): 13–25.
Schaner, S (2015), “Do Opposites Detract? Intrahousehold Preference Heterogeneity and Inefficient Strategic Savings”, American Economic Journal: Applied Economics 7(2): 135-74.
Shonchoy, A S (ed.) (2014), Seasonality and Microcredit, Springer, Tokyo.
Squires, M (2024), “Kinship Taxation as an Impediment to Growth: Experimental Evidence from Kenyan Microenterprises”, The Economic Journal 134(662): 2558–2579.
Stiglitz, J (1975), “The effects of income, wealth, and capital gains taxation on risk-taking.” Stochastic Optimization Models in Finance 291–311.
Stiglitz, J, and A Weiss (1981), “Credit rationing in markets with imperfect information.” American Economic Review 71(3): 393–410
Stiglitz, J E (1990), “Peer Monitoring and Credit Markets”, The World Bank Economic Review 4(3): 351–366.
Townsend, R M (1979), “Optimal contracts and competitive markets with costly state verification.” Journal of Economic Theory 21(2): 265–293.
Varian, H R (1990), “Monitoring Agents with Other Agents”, Journal of Institutional and Theoretical Economics 146(1): 153–174.
Contact VoxDev
If you have questions, feedback, or would like more information about this article, please feel free to reach out to the VoxDev team. We’re here to help with any inquiries and to provide further insights on our research and content.